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All Journal IJMST Scientific Journal of Engineering Research Methods in Science and Technology Studies
Immunhierokene Clinton Obrorindo
Petroleum Training Institute
Computer Science & IT Engineering

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Deep Learning–Driven Anomaly Detection for IoT-Enabled Smart Engineering Systems Godfrey Perfectson Oise; Kevin Chinedu Pius; Felix Oshiorenoya Uloko; Immunhierokene Clinton Obrorindo; Roli Lydia Oshasha
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.432

Abstract

The rapid adoption of Internet of Things (IoT) technologies in smart engineering systems has increased the need for reliable anomaly detection mechanisms capable of identifying cyberattacks, operational faults, and abnormal system behaviors in complex cyber–physical environments. Existing rule-based and conventional machine learning approaches often struggle to effectively model the non-linear, high-dimensional, and highly imbalanced nature of IoT-generated multivariate time-series data, thereby limiting their capability to detect subtle and previously unseen anomalies. To address these challenges, this study proposes a deep learning–driven anomaly detection framework based on a hybrid CNN–LSTM autoencoder architecture for modeling spatiotemporal system behavior in IoT-enabled engineering environments. The proposed framework integrates convolutional neural networks for spatial feature extraction with long short-term memory networks for temporal dependency learning, while anomaly detection is performed using reconstruction error analysis and adaptive thresholding under unsupervised learning conditions. Experimental evaluation was conducted using the BATADAL-A dataset, which represents a realistic cyber–physical water distribution system. The results demonstrate stable convergence and strong generalization performance, with closely aligned training and validation losses throughout the learning process. The proposed framework achieved 90% overall accuracy, anomaly precision of 0.83, anomaly recall of 0.22, and an AUC of 0.677, indicating effective modeling of normal operational behavior but limited sensitivity to rare anomalous events. These findings demonstrate that the proposed CNN–LSTM autoencoder provides reliable low–false alarm monitoring for IoT-enabled smart engineering systems while highlighting the need for future improvements to enhance anomaly sensitivity and robustness in safety-critical applications.
Energy-Efficient Federated Learning with Temporal Convolutional Networks for Intrusion Detection Godfrey Perfectson Oise; Felix Oshiorenoya Uloko; Kevin Chinedu Pius; Roli Lydia Oshasha; Eric Edeigue Osemwegie; Immunhierokene Clinton Obrorindo
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.462

Abstract

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the attack surface of modern network infrastructures, necessitating intelligent and scalable intrusion detection systems. Federated Learning (FL) has emerged as a promising paradigm for distributed model training without centralized data sharing; however, challenges such as energy efficiency, data heterogeneity, and privacy preservation remain inadequately addressed. Existing studies often emphasize optimization objectives theoretically without validating them under realistic constraints. This paper proposes an energy-aware federated learning framework integrating Temporal Convolutional Networks (TCNs) for intrusion detection using distributed network traffic data. The framework incorporates differential privacy for secure model updates and a conceptual energy-aware client participation strategy. Experiments are conducted on the UNSW-NB15 dataset under a controlled setting with fixed client participation and communication parameters. The results demonstrate that the proposed model achieves improved classification accuracy and stable convergence behavior across communication rounds while operating under a fixed energy budget. However, energy consumption remains constant due to controlled experimental conditions, indicating that the study evaluates performance under energy constraints rather than dynamic energy optimization. The findings highlight the effectiveness of TCN-based federated models for intrusion detection in resource-constrained environments. Future work will focus on dynamic energy modeling, heterogeneous client environments, and comprehensive multi-objective evaluation.
Facial Expression Recognition Using a Sequential Convolutional Neural Network for Multi-Class Classification Godfrey Oise; Immunhierokene Clinton OBRORINDO; Roli Lydia OSHASHA; Kevin Chinedu PIUS; Felix Oshiorenoya ULOKO
Indonesian Journal of Modern Science and Technology Vol. 2 No. 1 (2026): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/

Abstract

Facial Emotion Recognition (FER) has become an important area of research in affective computing, human–computer interaction, intelligent surveillance, and healthcare applications due to its ability to automatically identify and interpret human emotional states from facial expressions. This study presents a lightweight Sequential Convolutional Neural Network (S-CNN) framework for multi-class facial emotion recognition using facial expression images categorized into eight emotional classes: Anger, Contempt, Disgust, Fear, Happy, Neutral, Sad, and Surprised. The proposed framework integrates image preprocessing, data augmentation, convolutional feature extraction, and deep learning-based classification to develop an efficient and computationally lightweight emotion recognition system. The model was trained and evaluated using a publicly available facial expression dataset, with performance assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrated strong classification performance, achieving an overall accuracy of 92%, a weighted precision of 87%, a weighted recall of 92%, and a weighted F1-score of 89%. The confusion matrix further revealed effective discrimination among most emotional categories, with only minor misclassification observed between visually similar expressions. Comparative analysis with established deep learning architectures reported in the literature, including VGG16, MobileNetV2, ResNet50, and EfficientNet-B0, highlights the potential effectiveness of the proposed lightweight architecture while maintaining lower computational complexity. The findings demonstrate that simplified CNN architectures can provide accurate and efficient facial emotion recognition, making them suitable for real-time and resource-constrained applications. Future research should explore larger benchmark datasets, cross-dataset validation, and advanced deep learning architectures to further improve generalization and robustness.
Co-Authors Eric Edeigue Osemwegie Felix Oshiorenoya Uloko Godfrey Oise Godfrey Perfectson Oise Kevin Chinedu Pius Roli Lydia Oshasha